def main():
"""Prepares data for the person recognition demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking live demo script')
parser.add_argument('-i',
type=str,
nargs='+',
help='Input sources (indexes \
of cameras or paths to video files)',
required=True)
parser.add_argument('-m',
'--m_detector',
type=str,
required=True,
help='Path to the person detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the person detection model')
parser.add_argument('--m_reid',
type=str,
required=True,
help='Path to the person reidentification model')
parser.add_argument('--output_video', type=str, default='', required=False)
parser.add_argument('--config', type=str, default='', required=False)
parser.add_argument('--history_file', type=str, default='', required=False)
parser.add_argument('-d', '--device', type=str, default='CPU')
parser.add_argument('-l',
'--cpu_extension',
help='MKLDNN (CPU)-targeted custom layers.Absolute \
path to a shared library with the kernels impl.',
type=str,
default=None)
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
args = parser.parse_args()
capture = MulticamCapture(args.i)
log.info("Creating Inference Engine")
ie = IECore()
person_detector = Detector(ie, args.m_detector, args.t_detector,
args.device, args.cpu_extension,
capture.get_num_sources())
if args.m_reid:
person_recognizer = VectorCNN(ie, args.m_reid, args.device)
else:
person_recognizer = None
run(args, capture, person_detector, person_recognizer)
log.info('Demo finished successfully')
def main():
current_dir = os.path.dirname(os.path.abspath(__file__))
"""Prepares data for the person recognition demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking live demo script')
parser.add_argument('-i',
type=str,
nargs='+',
help='Input sources (indexes \
of cameras or paths to video files)',
required=True)
parser.add_argument('--config',
type=str,
default=os.path.join(current_dir, 'config.py'),
required=False,
help='Configuration file')
parser.add_argument('--detections',
type=str,
help='JSON file with bounding boxes')
parser.add_argument('-m',
'--m_detector',
type=str,
required=False,
help='Path to the person detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the person detection model')
parser.add_argument('--m_segmentation',
type=str,
required=False,
help='Path to the person instance segmentation model')
parser.add_argument(
'--t_segmentation',
type=float,
default=0.6,
help='Threshold for person instance segmentation model')
parser.add_argument('--m_reid',
type=str,
required=True,
help='Path to the person re-identification model')
parser.add_argument('--output_video',
type=str,
default='',
required=False,
help='Optional. Path to output video')
parser.add_argument(
'--history_file',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save results of the demo'
)
parser.add_argument(
'--save_detections',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save bounding boxes')
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
parser.add_argument('-d', '--device', type=str, default='CPU')
parser.add_argument('-l',
'--cpu_extension',
help='MKLDNN (CPU)-targeted custom layers.Absolute \
path to a shared library with the kernels impl.',
type=str,
default=None)
parser.add_argument('-u',
'--utilization_monitors',
default='',
type=str,
help='Optional. List of monitors to show initially.')
args = parser.parse_args()
if check_detectors(args) != 1:
sys.exit(1)
if len(args.config):
log.info('Reading configuration file {}'.format(args.config))
config = read_py_config(args.config)
else:
log.error(
'No configuration file specified. Please specify parameter \'--config\''
)
sys.exit(1)
random.seed(config['random_seed'])
capture = MulticamCapture(args.i)
log.info("Creating Inference Engine")
ie = IECore()
if args.detections:
person_detector = DetectionsFromFileReader(args.detections,
args.t_detector)
elif args.m_segmentation:
person_detector = MaskRCNN(ie, args.m_segmentation,
args.t_segmentation,
args.device, args.cpu_extension,
capture.get_num_sources())
else:
person_detector = Detector(ie, args.m_detector, args.t_detector,
args.device, args.cpu_extension,
capture.get_num_sources())
if args.m_reid:
person_recognizer = VectorCNN(ie, args.m_reid, args.device,
args.cpu_extension)
else:
person_recognizer = None
run(args, config, capture, person_detector, person_recognizer)
log.info('Demo finished successfully')
def main():
"""Prepares data for the person recognition demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking live demo script')
parser.add_argument('-i',
type=str,
nargs='+',
help='Input sources (indexes \
of cameras or paths to video files)',
required=True)
#parser.add_argument('-m', '--m_detector', type=str, required=True, help='Path to the person detection model')
parser.add_argument('-m',
'--m_detector',
type=str,
default='model/person-detection-retail-0013.xml')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the person detection model')
#parser.add_argument('--m_reid', type=str, required=True, help='Path to the person reidentification model')
parser.add_argument(
'--m_reid',
type=str,
default='model/person-reidentification-retail-0031.xml')
parser.add_argument('--output_video', type=str, default='', required=False)
#parser.add_argument('--config', type=str, default='', required=False)
parser.add_argument('--config',
type=str,
default='config.py',
required=False)
parser.add_argument('--history_file', type=str, default='', required=False)
parser.add_argument('-d', '--device', type=str, default='CPU')
#parser.add_argument('-d', '--device', type=str, default='MYRIAD')
parser.add_argument('-l',
'--cpu_extension',
help='MKLDNN (CPU)-targeted custom layers.Absolute \
path to a shared library with the kernels impl.',
type=str,
default=None)
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
args = parser.parse_args()
# 위에서 가져온 옵션에 대한 것들 args에 저장됨.
# args.i 해서 옵션 i 에 저장된것 가져올 수 있음
capture = MulticamCapture(
args.i) # video.py 에 있는 클래스 변수에 포인터 지정함. capture 호출해서 그 안에 있는 함수 사용가능
log.info("Creating Inference Engine")
ie = IECore(
) #추론엔진 인터페이스 지정하기 (https://docs.openvinotoolkit.org/2019_R3/classie__api_1_1IECore.html)
person_detector = Detector(ie, args.m_detector, args.t_detector,
args.device, args.cpu_extension,
capture.get_num_sources())
# capture.get_num_sources() : 영상의 갯수
if args.m_reid:
person_recognizer = VectorCNN(ie, args.m_reid, args.device)
else:
person_recognizer = None
# 20200511 추가
jot = JotTable()
run(args, capture, person_detector, person_recognizer, jot)
log.info('Demo finished successfully')
def main():
current_dir = os.path.dirname(os.path.abspath(__file__))
"""Prepares data for the object tracking demo"""
parser = argparse.ArgumentParser(description='Multi camera multi object \
tracking live demo script')
parser.add_argument(
'-i',
'--input',
required=True,
nargs='+',
help=
'Required. Input sources (indexes of cameras or paths to video files)')
parser.add_argument('--loop',
default=False,
action='store_true',
help='Optional. Enable reading the input in a loop')
parser.add_argument('--config',
type=str,
default=os.path.join(current_dir, 'configs/person.py'),
required=False,
help='Configuration file')
parser.add_argument('--detections',
type=str,
help='JSON file with bounding boxes')
parser.add_argument('-m',
'--m_detector',
type=str,
required=False,
help='Path to the object detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the object detection model')
parser.add_argument('--m_segmentation',
type=str,
required=False,
help='Path to the object instance segmentation model')
parser.add_argument(
'--t_segmentation',
type=float,
default=0.6,
help='Threshold for object instance segmentation model')
parser.add_argument(
'--m_reid',
type=str,
required=True,
help='Required. Path to the object re-identification model')
parser.add_argument('--output_video',
type=str,
default='',
required=False,
help='Optional. Path to output video')
parser.add_argument(
'--history_file',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save results of the demo'
)
parser.add_argument(
'--save_detections',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save bounding boxes')
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
parser.add_argument('-d', '--device', type=str, default='CPU')
parser.add_argument('-u',
'--utilization_monitors',
default='',
type=str,
help='Optional. List of monitors to show initially.')
args = parser.parse_args()
if check_detectors(args) != 1:
sys.exit(1)
if len(args.config):
log.debug('Reading config from {}'.format(args.config))
config = read_py_config(args.config)
else:
log.error(
'No configuration file specified. Please specify parameter \'--config\''
)
sys.exit(1)
random.seed(config.random_seed)
capture = MulticamCapture(args.input, args.loop)
log.info('OpenVINO Runtime')
log.info('\tbuild: {}'.format(get_version()))
core = Core()
if args.detections:
object_detector = DetectionsFromFileReader(args.detections,
args.t_detector)
elif args.m_segmentation:
object_detector = MaskRCNN(core, args.m_segmentation,
config.obj_segm.trg_classes,
args.t_segmentation, args.device,
capture.get_num_sources())
else:
object_detector = Detector(core, args.m_detector,
config.obj_det.trg_classes, args.t_detector,
args.device, capture.get_num_sources())
if args.m_reid:
object_recognizer = VectorCNN(core, args.m_reid, args.device)
else:
object_recognizer = None
run(args, config, capture, object_detector, object_recognizer)
from config import Config
from mc_tracker.mct import MultiCameraTracker
from mc_tracker.sct import SingleCameraTracker
# REID_MODEL
from utils.network_wrappers import VectorCNN
logger = logging.getLogger("api")
config = Config()
os.environ['CUDA_VISIBLE_DEVICES'] = config.DEVICE_ID
app = FastAPI()
number_of_cameras = 1
reid = VectorCNN(config)
global tracker
tracker = {}
class RepeatTimer(Timer):
def run(self):
while not self.finished.wait(self.interval):
self.function(*self.args, **self.kwargs)
def update_db():
try:
global parsed_date, parsed_time, tracker
d, t = datetime.now().strftime("%Y-%m-%d/%H:00:00").split('/')
if (t != parsed_time):
